Effect of Raloxifene on Stroke and Venous Thromboembolism According to Subgroups in Postmenopausal Women at Increased Risk of Coronary Heart Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Raloxifene, a selective estrogen receptor modulator, reduces risk of invasive breast cancer and osteoporosis, but the effect on risk for stroke and venous thromboembolism in different patient subgroups is not established. The purpose of this analysis was to evaluate the effect of raloxifene on the incidence of all strokes, stroke deaths, and venous thromboembolic events according to participant subgroups. METHODS: This was a secondary end point analysis of an international, randomized, placebo-controlled clinical trial of 10 101 postmenopausal women with or at increased risk of coronary heart disease followed a median of 5.6 years. Strokes, venous thromboembolic events, and deaths were adjudicated by expert centralized committees. Strokes were categorized as ischemic, hemorrhagic, or undetermined and venous thromboembolic events were subclassified. RESULTS: The incidences of all strokes did not differ between raloxifene (incidence rate per 100 woman-years=0.95) and placebo (incidence rate=0.86) treatment groups (P=0.30). In women assigned raloxifene versus placebo, there was a higher incidence of fatal strokes (incidence rates=0.22 and 0.15, respectively, P=0.0499) and venous thromboembolic events (incidence rates=0.39 and 0.27, respectively, P=0.02). No significant subgroup interactions were found except that there was a higher incidence of stroke associated with raloxifene use among current smokers. CONCLUSIONS: In postmenopausal women at increased risk for coronary events, the incidences of venous thromboembolism and fatal stroke but not all strokes were higher in those assigned raloxifene versus placebo. Raloxifene's effect did not differ across subgroups, except that the risk of stroke differed by smoking status. Treatment decisions about raloxifene should be based on a balance of projected absolute risks and benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".